The annotation of brain lesion images is a key step in clinical diagnosis and treatment of a wide spectrum of brain diseases. In recent years, segmentation methods based on deep learning have gained unprecedented popularity, leveraging a large amount of data with high-quality voxel-level annotations. However, due to the limited time clinicians can provide for the cumbersome task of manual image segmentation, semi-supervised medical image segmentation methods present an alternative solution as they require only a few labeled samples for training. In this paper, we propose a novel semi-supervised segmentation framework that combines improved mean teacher and adversarial network. Specifically, our framework consists of (i) a student model and a teacher model for segmenting the target and generating the signed distance maps of object surfaces, and (ii) a discriminator network for extracting hierarchical features and distinguishing the signed distance maps of labeled and unlabeled data. Besides, based on two different adversarial learning processes, a multi-scale feature consistency loss derived from the student and teacher models is proposed, and a shape-aware embedding scheme is integrated into our framework. We evaluated the proposed method on the public brain lesion datasets from ISBI 2015, ISLES 2015, and BRATS 2018 for the multiple sclerosis lesion, ischemic stroke lesion, and brain tumor segmentation respectively. Experiments demonstrate that our method can effectively leverage unlabeled data while outperforming the supervised baseline and other state-of-the-art semi-supervised methods trained with the same labeled data. The proposed framework is suitable for joint training of limited labeled data and additional unlabeled data, which is expected to reduce the effort of obtaining annotated images.

脑损伤图像的注释是临床诊断和治疗多种脑疾病的关键步骤。近年来，基于深度学习的分割方法获得了前所未有的普及，它利用了大量具有高质量体素级注释的数据。然而，由于临床医生可以提供手动图像分割的繁琐任务的时间有限，半监督医学图像分割方法提供了一种替代解决方案，因为它们只需要几个标记样本进行训练。在本文中，我们提出了一种新颖的半监督分割框架，该框架结合了改进的平均教师和对抗网络。具体来说，我们的框架包括（i）学生模型和教师模型，用于分割目标并生成物体表面的有符号距离图，(ii) 一个鉴别器网络，用于提取分层特征并区分标记和未标记数据的有符号距离图。此外，基于两种不同的对抗性学习过程，提出了一种源自学生和教师模型的多尺度特征一致性损失，并将形状感知嵌入方案集成到我们的框架中。我们在 ISBI 2015、ISLES 2015 和 BRATS 2018 的公共脑损伤数据集上分别评估了所提出的方法，用于多发性硬化病灶、缺血性中风病灶和脑肿瘤分割。实验表明，我们的方法可以有效地利用未标记的数据，同时优于监督基线和其他使用相同标记数据训练的最先进的半监督方法。